We introduce Rotateit, a system that enables fingertip-based object rotation along multiple
axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it …

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

Learning to solve tasks from a sparse reward signal is a major challenge for standard
reinforcement learning (RL) algorithms. However, in the real world, agents rarely need to …

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …

Policy optimization methods are powerful algorithms in Reinforcement Learning (RL) for
their flexibility to deal with policy parameterization and ability to handle model …

Our ultimate goal is to build robust policies for robots that assist people. What makes this
hard is that people can behave unexpectedly at test time, potentially interacting with the …

Multitask Reinforcement Learning (MTRL) approaches have gained increasing attention for
its wide applications in many important Reinforcement Learning (RL) tasks. However, while …

Deep reinforcement learning (DRL) is playing an increasingly important role in real-world
applications. However, obtaining an optimally performing DRL agent for complex tasks …

In the last decade, the increased availability of powerful computing machinery has led to an
increasingly widespread application of machine learning methods. Machine learning has …

Reinforcement learning (RL) provides a formalism for learning-based control. By attempting
to learn behavioral policies that can optimize a user-specified reward function, RL methods …